This invention relates generally to acoustic well logging, and in particular to estimating formation slowness using an acoustic well logging tool.
Formation compressional and shear slowness are two of the most important parameters used in the exploration and production of hydrocarbon. Conventionally, they are measured by sonic logging. A sonic logging tool consists of two primary parts: data acquisition and data processing. Data acquisition is done by sending a logging tool down into an exploration or production well, and the acoustic source on the logging tool sends an acoustic signal that subsequently propagates along the well and is recorded at several evenly spaced receivers that are some distance away from the source. Formation compressional and shear slowness are then estimated by processing the recorded waveforms, using array sonic processing techniques, such as the slowness-time coherence method (STC).
Recent studies have shown that STC yields an accurate slowness estimation when, and only when, the acoustic waves propagated along a wellbore are non-dispersive, or multiple arrivals contained in the waveforms are well separated in the slowness-time domain. When the underlying waveforms are dispersive or the waveforms compose of mixed modes with similar group velocities, such as in the case of wireline leaky P-mode (for compressional slowness in very slow formation), wireline dipole mode (for shear slowness), quadrupole mode (for shear slowness) in logging while drilling (LWD) or some monopole mode (for compressional slowness) in LWD, STC produces a systematic error in the slowness estimation. The amount of those systematic errors is large enough to result in negative implications in the exploration and production of hydrocarbons, such as oil can be mistaken as water in prospect assessment. Furthermore, the correlogram produced by STC method, currently used as a quality control tool for slowness estimation by industry, does not reflect the accuracy of the slowness estimation.
Recently, several approaches have been developed to address the limitations of the existing methods and apparatus for estimating formation slowness. Theses fall into two categories: model-driven dispersion correction and phase velocity processing. The model-driven dispersion correction approaches have been adapted by major logging companies, such as Schlumberger and Baker Hughes. They have been developed to address wireline dipole mode and leaky P mode. Baker Hughes also applies its approach to their quadrupole LWD data. The model-driven approach still makes use of STC and corrects dispersion effects by applying a theoretically calculated dispersion curve of the corresponding mode. The dispersion correction of the model-driven approach is only accurate under several assumptions that are hard to meet in reality, including circular borehole, homogeneous and isotropic formation and good knowledge of a variety of formation and mud properties, including slowness and density. In the case where the waveforms contain multiple arrivals that are not well separated in slowness-time domain, all model-driven methods will not arrive at the correct formation slowness. The phase velocity processing approach estimate instantaneous phase slowness for each receiver pairs, which could potential yield more accurate slowness estimation than STC method. However as the formation slowness value is computed by averaging over the desired travel time interval, the phase velocity processing still generates a systematic error in slowness estimation. Also the phase velocity processing may suffer some stability problems.
The present invention is directed to overcoming one or more of the limitations of the existing methods and apparatus for estimating formation slowness.